Salīdzināt metodes

	Mašīnmācīšanās papildinātā tendenču rezultātu saskaņošana	Coarsened Exact Matching (CEM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2004	2011-2012
Autors	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Iacus, King, & Porro
Tips	Causal inference / matching	Matching / causal inference
Pirmavots	McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Citi nosaukumi	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	CEM, coarsened matching, monotonic imbalance bounding matching
Saistītās	6	6
Kopsavilkums	Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.